A modular PyTorch library that provides state-of-the-art pretrained diffusion models for image, audio, and video generation. It supplies interchangeable building blocks, noise schedulers, and inference pipelines that developers can run in a few lines of code.
Project overview
The project supplies simple inference pipelines, interchangeable noise schedulers, and customizable building-block models for diffusion systems in PyTorch.
Project type
Model Development · Image & Vision · Video · Audio & Speech
Use cases
Image Processing · Video Creation
Deployment
Library integration
License
Apache-2.0
Best for
Developers, researchers, and AI engineers who build and train custom diffusion systems for image, audio, and video generation.
Key capabilities
State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code.
Interchangeable noise schedulers for different diffusion speeds and output quality.
Pretrained models that can be used as building blocks for creating custom end-to-end diffusion systems.
Guides and functionality for training diffusion models for different tasks with different training techniques.
Functionality and guides to optimize diffusion models to run faster and consume less memory.
Limitations and risks
Relies on PyTorch as its primary deep learning framework.
Getting started
Install the library via pip or conda, for instance using pip install diffusers[torch]. Following installation, load a pretrained model and generate an image in Python.
Alternatives and comparisons
Provides an all-in-one training suite for diffusion models runnable via GUI or CLI on consumer-grade hardware.
Packages AI models into free, no-install GUI tools covering image processing, video enhancement, and text-to-speech for non-technical users.
Provides a collection of example workflows as images with embedded metadata that can be loaded directly into ComfyUI to replicate the generation process.
GitHub project description: 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
README: 🤗 Diffusers offers three core components: - State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code. - Interchangeable noise schedulers for…
README: 🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models
README: Guides for how to train a diffusion model for different tasks with different training techniques.
Release: Diffusers 0.39.0: New image and video pipelines, core library improvements, and more